Speaker
Description
Phenotypic plasticity in breast cancer can be viewed as stochastic switching between stable gene-expression states associated with clinically distinct subtypes. Building on our previously published NF-κB-centered regulatory network model for breast cancer heterogeneity \cite{Lopes2025}, I will present recent results showing that, in non-conservative gene regulatory systems, in which potential functions are generally not uniquely defined, geometric features of phase space offer a well-defined framework for describing transition probabilities and timescales. In particular, the distance between stable and unstable stationary states, together with the bifurcation structure organizing multistability, emerges as a robust determinant of transition accessibility, transition times, and variability. The analysis reveals a marked asymmetry between phenotypic regimes: the HER2+ attractor is comparatively robust to intrinsic parameter variation, whereas the TNBC regime strongly amplifies such variation, offering a dynamical interpretation for the pronounced heterogeneity observed in triple-negative breast cancer. More broadly, the results support a geometric framework for phenotypic transitions in gene regulatory networks operating far from equilibrium \cite{Caldas2026}.
Bibliography
@article{Lopes2025,
author = {Lopes, Francisco and Pires, B. R. and Lima, A. A. and Binato, Rodolfo and Abdelhay, Eliana},
title = {NF-{$\kappa$}B epigenetic attractor landscape drives breast cancer heterogeneity},
journal = {npj Systems Biology and Applications},
volume = {11},
number = {1},
pages = {135},
year = {2025}
}
@article{Caldas2026,
author = {Caldas, Mayara D. A. and Lima, Alexandre A. B. and Lopes, Francisco},
title = {Phase-space distance between stationary states modulates phenotypic plasticity in breast cancer},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.03.06.710190},
note = {bioRxiv 2026.03.06.710190}
}
